KR101435305B1 - Location aware recommendation engine - Google Patents

Abstract

The subject matter disclosed herein relates to a location awareness recommendation engine. In response to the recommendation request, the associated recommendation may be ranked based on the user's current location, the location associated with the entity, and / or the accessibility criteria.

Description

{LOCATION AWARE RECOMMENDATION ENGINE}

Priority claim under 35 USC §119

This patent application claims the benefit of U.S. Provisional Patent Application No. 61 / 297,666 entitled " Location-aware Ordering of Search Results "filed on January 22, 2010, which is assigned to the assignee hereof and incorporated herein by reference .

Technical field

Generally, the subject matter disclosed herein relates to a location awareness recommendation engine.

Information:

Different methods may be used to determine the location of a mobile station (MS), such as a cell phone, tablet, personal digital assistant (PDA), e-book reader, smartbook, netbook, or any other mobile station have. For example, some MSs may use a Satellite Positioning System (SPS) such as Global Positioning System (GPS), or a combination of SPS and cellular base stations to determine their location.

Many mobile stations have the ability to perform a search for a place, for example, through a web-based search engine or a mapping application. The search results returned by the search engine may be displayed in order of relevancy. For example, the search string "fast food" might return general information about fast food restaurants such as KFC's official web address and a few fast-food restaurants in your metropolitan area. Such information may not be immediately available to pedestrians seeking food at lunchtime, as they are located within shopping malls or amusement parks. The mapping application may display the user's current location on the map and provide, for example, fast food restaurants located within two miles of the user, where the distance between the particular fast food restaurant and the user is a straight line between the location of the restaurant and the location of the user It is determined using the distance. Likewise, these results may not be of benefit to pedestrians, for example, those who are in the airport and are trying to eat food within 10 minutes to board a flight.

In a particular design example, a method is provided for ranking recommendations for a pedestrian environment. A search request may be received. In response to the search request, one or more search results associated with the pedestrian environment may be determined; The location of the mobile station associated with the search request may be determined; At least a portion of the one or more search results may be ranked, based at least in part on at least one of a location of the mobile station and a location and / or accessibility criteria associated with at least a portion of the one or more search results. It should be understood, however, that this is merely exemplary design practice and that the subject matter claimed is not limited in this respect.

Non-exhaustive and non-exhaustive features will be discussed with reference to the following drawings, wherein like reference numerals refer to like elements throughout the various views.
Figure 1A illustrates one floor (ground floor) of an indoor shopping venue and a user carrying a mobile station moving within the site.
FIG. 1B illustrates two layers of the indoor shopping scene of FIG. 1A.
Figure 2 illustrates the entities in and near the shop floor and a database listing information about these entities.
Figure 3 illustrates a mobile station displaying location aware ordering of recommendations for an indoor shopping scene.
Figure 4 illustrates a method for generating a location aware alignment of the suggestions of Figure 3;
Figure 5 illustrates a mobile station displaying another example of location awareness alignment of recommendations.
Figure 6 illustrates a mobile station displaying another example of location awareness alignment of recommendations.
Figure 7 is an exemplary flow chart for generating a location aware alignment of recommendations.
8 illustrates a block diagram of a system for communicating with a mobile station.

Reference throughout this specification to "one example", "one feature", "instance", or "feature" means that a feature associated with that feature and / or example And / or < / RTI > example. Therefore, these phrases appearing in various places in this specification do not necessarily indicate the same features and / or examples. Specific descriptions of features, examples, and / or functions may be combined in one or more of the features, examples, and / or functions. In this specification, the terms "position" and "position" may be used interchangeably. In this specification, the terms "search request" and "recommendation request" may be used interchangeably, and the terms "search result" and "recommendation result" may also be used interchangeably.

The pedestrian environment may mean an environment or area that can be accessed by pedestrians. For example, a pedestrian environment may include an area where a pedestrian may walk, run, wheelchair, ride a bicycle, or otherwise physically move from one location to another. Examples of pedestrian environments may include indoor and outdoor environments. Examples of indoor pedestrian environments include office buildings, hotels, shopping malls, warehouses, green grocery stores, casinos, museums, transportation terminals (e.g., airports, subway stations, ferries / cruise terminals, etc.) , Convention centers, and sports stadiums. Examples of outdoor pedestrian environments include a number of possible examples, such as beaches, boardwalks, amusement parks, zoos, outdoor shopping malls / strips, outdoor markets, parks, and pedestrians Including areas that have a pathway, such as a sidewalk.

Navigation for pedestrians in an indoor environment requires a different approach from navigation services for the driver of a vehicle. For example, in an outdoor environment where a clear view of the transmitters of these signals may be blocked, such as in an interior structure (for example, in a shopping mall, an airport, an office building, etc.) or in midtown Manhattan In an outdoor urban canyon, signals such as GPS signals or cellular signals may deteriorate and become unreliable. Also, pedestrian sites often have obstacles such as stairs, elevators, and escalators, and barriers such as restricted access areas, which may add to the complexity of navigation. Stairs, elevators and escalators not only provide physical obstacles to the pedestrian's path but also take pedestrians out of his / her current floor / location context and make him / her new and unfamiliar / It also shows the floor changes that can be placed in the location context. The amount of points of interest (POIs), such as restaurants, shops, theaters, restrooms, or other entities that pedestrians can access at any point in time, is limited by the ease of walking And may be limited to the area within. Therefore, if a pedestrian enters a search request in a search application on his / her mobile station, the POIs in the search results are limited to those that fall within easy walking distance (or travel distance if the pedestrian is in a wheelchair) It may be helpful to rank the results in the order of ease of accessibility.

FIG. 1A illustrates a pedestrian user carrying a mobile station 100 and a first floor of an indoor shopping mall. 1B illustrates the second floor of the shopping mall. Navigation signals from SPS, such as GPS, may not be available in the mall. There may be ways in which the location of the mobile station 100 can be determined within an area where the SPS signal is not available. There may be devices within the area that have known fixed locations. For example, wireless devices having fixed and known locations may be distributed throughout the moles of FIGS. 1A and 1B. These wireless devices can transmit signals, and the position of the mobile station can be determined using trilateration techniques based on these signals. For example, in an area where SPS signals are not available, the mobile station may estimate its location by using signals involving nearby wireless devices. For example, these signals may include Institute of Electrical and Electronics Engineers (IEEE) 802.11-compliant Wi-Fi signals, and signals associated with femtocells, Bluetooth signals, and the like.

Pedestrian field operators may provide access to voice or data networks as an alternative to or as an extension to cellular cellular signals by installing more and more wireless devices such as Wi-Fi access points or femtocells These signals can degrade or become unreliable in an indoor pedestrian environment. For example, Wi-Fi access points (not shown in FIGS. 1A and 1B) may be installed throughout the shopping mall. The position of the user's mobile station (and hence the user's position) may be determined using, for example, trilateration techniques using Wi-Fi signals. The mobile station 100 may determine its location, for example, by performing an operation on its own, by transmitting a location request to the server and receiving the calculated location from the server. When the user enters the mall, the mobile station 100 may initiate communication with the Wi-Fi access points in the mall and indicate through the position determination using the Wi-Fi signals that the MS 100 is located within the mall can do. If a determination is made that the MS 100 is located in the mall, a map (e.g., FIG. 1A) of the layer in which the user is located is obtained by the MS 100 or sent to the MS 100, Lt; / RTI > Alternatively, for example, the map may be preloaded on MS 100 before the user enters the mall. The location of the user in the mall may be indicated by a silhouette picture on a map on the screen of the MS 100 (e.g., FIG. 1A). In Figure 1A, the user is moving in the direction of the elevator.

The location of entities and structures within the mall may be represented by a coordinate system, which may be a local coordinate system or a generalized global coordinate system, such as the WGS84 coordinate system used with GPS. 1a and 1b, the coordinate system has an (x, y, z) format, where x represents the position of an entity, structure, etc. along the horizontal axis of Figs. 1a and 1b , y represents the position of the entity or structure along the vertical axis of Figs. 1a and 1b, and z represents the layer of the shopping mall, where 1 represents one layer (ground layer) and 2 represents two layers. For example, the user's position can be displayed as (18, 9, 1) and the entrance / exit 1 of the mall can be represented as (18, 10, 1) The entrance / exit of the SBARRO store at the floor can be indicated by (7, 9, 1), and the entrance / exit points of the escalator 2 connecting the 1st and 2nd floors can be represented by 8, 3 (1, 2) on the second floor, and the entrance / exit of the PIZZA HUT store on the second floor of the mall is indicated by (18, 7, 2) . For example, the intersection of entry and exit paths and corridors such as (7, 3, 1) can function as the intersections of the path graph, and the distance between these intersections can serve as the edge of the path graph have.

Figure 2 illustrates a database 200 listing entities and structures that exist in or near a shopping mall and information about such entities and structures. For example, such a database may be stored on a server controlled by a shopping mall. The database 200 may be transmitted to the user's MS 100 according to one design example. The first column of the database 200 represents the name of the entity or structure. The second column of database 200 represents the categories corresponding to the entities and structures in the first column. The third column represents any sub-categories of corresponding entities and structures. The fourth column shows the location of the entrances / exits of the corresponding entities and structures. The fifth column represents all other attributes of the corresponding entities and structures.

For example, BAJA FRESH belongs to the "food" category and the sub-categories "fast food" and "Mexican food". The inlet / outlet is located at (6, 3, 2) in Figure 2b. The elevator belongs to the "structure" category and the "floor change" sub-category. The inlets / outlets are located at (18, 14, 1) in the first floor and at (18, 14, 2) in the second floor. As shown in the "Other Attributes" column, disabled or wheelchair is accessible here. For the largest entities in the mall (for ice skating links, cinemas, and SEARS), the "Other Attributes" column contains the coordinates marking the boundaries of these entities. Restroom 3 is located inside the movie theater as indicated by the "location" column, and access is restricted as indicated by the "Other Attributes" column and is only available to movie audiences. Toilets 4 include only one toilets located within the Sears (second floor of the mall) as indicated by the "Location" column, and only one at a time, as indicated by the column "Other Properties". Sears sells a variety of items displayed by the "Other Attributes" column. Large entities such as cinemas or Sears may include their own maps that may be transmitted to the MS 100 when the user enters a movie theater or sears. The Steak House provides fine dining as indicated by the "sub-category" column and has a specific business hours that are different from the business hours of the mall as shown in the "other properties" column. The database 200 may contain more or less information than the information shown in FIG. 2, or may contain different information. For example, the database 200 may include detailed information on menu selection / pricing of the restaurant and detailed information on inventory items / prices of the store. If the POI properties are changed (e.g., a store goes out of the mall and another store enters, or a store extends its business hours during the holiday season, etc.), the database 200 is updated to reflect the new information It is possible. The current version of the database 200 or a link or pointer to the database 200 may be transmitted to the MS 100 whenever the user enters the mall (e.g., may be transmitted over Wi-Fi) . The database 200 may be a single database / data structure or a combination of databases / data structures.

Figure 3 illustrates a mobile station displaying a location awareness alignment of recommendations for a shopping mall. The display 302 of the MS 100 illustrates a location awareness recommendation application that receives user input in the window 304 and provides a ranked list of location awareness recommendations as a response. In other design examples, the location awareness recommendation interface may be incorporated within a conventional search engine interface. The user may enter a recommendation request in window 304 via the keyboard 316, via an on-screen keyboard (not shown), or through voice-text features of the MS 100, After entering the search request, the user may activate the recommendation process by typing the return key or by clicking on the "recommend" word on display 302, for example. In another design example, an automated recommendation request may be received. For example, the MS 100 or the server may determine that the MS 100 is in the mall as described above, access the calendar application and discover that it is now a holiday season, You can also enter requests automatically.

In the example shown in Fig. 3, the current time is 1:15 pm as indicated on the display 302, and a recommendation request of "cheeseburger" has been entered. In response, the location awareness recommendation engine provides a list of restaurants that can sell cheeseburgers within easy walking distance of the user, where the list includes the user's current location, locations associated with restaurants, and / or access Are ranked in order of accessibility (e.g., from the most accessible to the least accessible) based on likelihood criteria.

Accessibility criteria include the length of the route to the destination, the complexity of the route, the need to intervene, turn around, or reverse in the path to the destination, such as stairs, elevators, or escalators, Congestion, whether the route is open for business or limited access, the time or money required at the destination, and whether the route is related to leaving the venue at the current time. A method for generating a location aware alignment of the recommendations shown in FIG. 3 will be described in detail with respect to FIG.

Continuing with FIG. 3, a description of the various icons on the display 302 continues. In this example, the BURGER KING is ranked as the top recommendation for the cheeseburger, and the icon 306 with the forward arrow with respect to the direction of the MS 100 tells the user that he / / Indicates that she should continue to move forward on her path from her current position to the Burger King. Since the second ranked Wendy's are on the second floor of the mall, an icon 308 indicating the use of the stairs in the path to Wendy's and an icon 310 indicating the use of the elevator in the path to Wendy's are displayed do. Although the user may arrive on the second floor through the escalator, the icon for the escalator is not displayed because the escalator 1, the closest escalator to the user, is in Sears and the complexity of navigating through the sears It can be accompanied. Therefore, the stairs and the elevator are displayed as the easiest accessible options. The third-ranked McDonald's is on the first floor of the mall, but its path is related to reversing the user's current direction, as indicated by the icon 312. For example, the current direction of the user may be detected through one or more sensors within the MS 100, such as an accelerometer. For example, if the path based on the walking direction to the destination suggested by the MS 100 is within +/- 15 degrees from the user's current direction, then this path may be considered to have the same direction as the user's current path have. Because the fourth ranked SONIC is outside the mall, an icon 314 is displayed indicating that it is currently outside the scene. After four restaurant recommendations from above, the icons 318 indicate that they are reasonable choices. The fifth ranked Steak House is closed at the current time based on the information in the database 200 and thus the icon 320 indicates that the steakhouse is an irrational choice. Although the steakhouse is now closed, this is included in the list because the list may present the user with other options for the cheeseburger in the future, and the user may display additional information about the steakhouse, such as business hours 302 by clicking on the steakhouse link.

In order to find additional information on each of the reasonable choices, the user may click on the display 302 with a corresponding restaurant name link. In order to look at one of these restaurants, to call one of these restaurants, or to retrieve the map / walking directions to one of these restaurants, The corresponding link can be clicked on. For example, clicking on the map / walking link for the Burger King may provide a map as shown in FIG. 1A, for example, providing the user's current location on the display 302 in a silhouette- . The path up to the Burger King may be determined using a location awareness recommendation engine and may be determined based on, for example, the following discussion with respect to FIG. Navigation guidance may be provided. For example, the path may be highlighted by a series of flow arrows arranged along the path to the Burger King by lighting and flickering the path using high visibility colors such as yellow. As another example, "Walk straight", "Turn left at the intersection", "Continue straight", "Turn left in bathroom 1", "Keep straight", and "Turn right into Burger King" May also be provided, which may be visually provided, for example, via display 302, and / or may be provided audibly through a speaker device of MS 100. [ If the user does not make any selection after the recommendation screen is provided on the display 302, the MS 100 may assume that the highest ranked recommendation by default is the destination, in which case the highest ranking is Burger King. Therefore, as the user moves in the mall, the arrow displayed on the icon 306 may change direction to indicate the direction in which the user must move to follow the path to reach the Burger King. However, if the user moves a large distance from the location from which the search request was entered without making a selection, the ranking of the recommendation results may be dynamically reordered to reflect the changed location of the mobile station 100 relative to the location of the recommended entities (And may thus reflect the user's changed location).

When a recommendation request for a cheeseburger is received, a search may be invoked to determine one or more search results associated with the pedestrian environment. In this example, the location awareness recommendation engine may determine that recommendations should be made for restaurants selling cheeseburgers and that pedestrians should be comfortable accessible based on his / her current location. Since the current location of the MS 100 (and therefore the user's current location) is determined to be within the mall, a search may be performed to sell the cheeseburger and determine the relevant list of one or more entities that are located in or adjacent to the mall have. This search may be performed using a relevance search algorithm (e.g., employed by a conventional search engine) that can use the information in the database 200 to determine which entities are associated with the sale of cheeseburgers It is possible. The location awareness recommendation engine may determine one or more search results (e.g., by executing a relevance search algorithm) associated with the mall, and display (302) a list of ranked results by applying location aware criteria to the search results It can also be generated as it is. The relevance search algorithm may determine the attributes of the search request. For example, the attributes of the search request "cheeseburger" may be determined to be "fast food", "hamburger", and the like. The relevance search algorithm matches the determined attributes of the search request with the attributes of one or more entities associated with the pedestrian environment (e.g., by matching information using the information in the database 200) and associates each matched entity with one or more search results Quot; In this example, the relevance search algorithm may return a list of matching entities including Burger King, McDonald, Sonic, Steak House, and Wendy's. The relevance search algorithm can rank Burger King, McDonald, Sonic, and Wendy as the most relevant ones because these entities are fast-food hamburger restaurants and steakhouses can rank as partially relevant, The reason is that this is a fine restaurant and selling hamburgers may not be the main business area of the steak house. Therefore, relevance searches can determine that Burger King, McDonald, Sonic, and Wendy have the same rank in relevance, and can insert them in alphabetical order before the stake house in the search results. For example, if a recommendation for "sushi" is entered instead of "burger", the relevance search can determine that the attributes of the search request are "sushi" and "japanese food" It may decide that entities do not exist in or near the current mall. This may provide a search result indicating that a match for sushi is not found, and based on this search result, the recommendation engine may display "no sushi is available at this site" on display 302 May be displayed. In this case, the provided navigation guidance may be "no applicable map / guidance exists ". For example, if a recommendation for a "steak" is entered instead of a "cheeseburger ", then the relevance search may determine that the stake house is the only relevant entity within or near the current mall, Skip the overall path cost determination for the steak house (e.g., as discussed in connection with FIG. 4), and rank the steakhouse as the top recommendation.

Applying a location recognition criterion to a list of search results from a relevance search is now discussed in connection with FIG. 4, which illustrates a method of generating a location aware alignment of the recommendations shown in FIG. This method may determine the shortest path to an entity in the search results, and may, for example, apply a weight to a particular segment or path of the path. The shortest path may be determined, for example, based on a prior art shortest path algorithm that uses the user's current location as a starting point and uses the entrance location of the desired entity as the destination. For example, a Dijkstra's algorithm may be used. As discussed, the current location of the user may be determined using trilateration techniques based on signal sources such as, for example, Wi-Fi access points or femtocells. Applicable weights may be based on various accessibility criteria, which may include path movement time criteria, path complexity criteria, and / or availability criteria.

The path travel time reference may be a path distance between a location of the mobile station and a location associated with each of the search results; speed; Congestion on the path; Time on stairs; Time on the escalator; And / or time in the elevator.

Path complexity criteria include whether the path includes a step; Whether the path includes an escalator; Whether the path includes an elevator; Current direction of movement; And / or whether or not the path includes leaving the current scene.

The availability criteria include whether the entity associated with the pedestrian environment is open or closed; Whether the entity is access restricted; The time required at the entity, and / or the amount of money required at the entity. Each individual criterion can be assigned a weight value. The weights may be combined with the path length to determine the total path cost. Recommendations can be ranked based on the total path cost of each referral. In some cases, one or more paths from an user's current direction to an entity may be determined to have a path that is similar to or closest to the shortest path. In one design example, the total path cost may be determined for each of these alternative paths, and the path with the lowest overall path cost will be used in the location recognition rank of the search results. In other designs, a suitable number of alternative paths (e.g., two or three paths) with similar overall path costs may be used in the location recognition rank of the search results.

For Burger King, the database 200 indicates that the only entry of the Burger King is at (7, 7, 1). The shortest path from the current user position (18, 9, 1) to (7, 7, 1) in FIG. 1A uses the local coordinate map of FIG. 1A and sets the start point as (18, 9, 1) May be determined by applying the conventional shortest path algorithm with points (7, 7, 1). This path can be determined to be (18, 9, 1) -> (18, 12, 1) -> (13,12,1) -> (7,12,1) -> (7,7,1) . The length of the path segment is 3 units between (18, 9, 1) and (18, 12, 1) and 5 units between (18, 12, 1) and (13, 12, 1) 12, 1) and (7, 12, 1), and 5 units between (7, 12, 1) and (7, 7, 1). Depending on the coordinate system used, one unit may be, for example, a measure of any suitable distance, for example, 1 meter, 10 meters, 1 yard, 10 yards, 1 foot, 10 feet, The length of the path may be determined as the sum of the lengths of the segments, which is 19 in this example. The weights may be applied to each segment or the entire path. In this example, the path from the user's current location to the entrance of the Burger King does not include, for example, steps, elevators, escalators, crowds, malls leaving, access restrictions, For example, 1) may be applied to each segment. For example, a segment containing a stair or congestion may be assigned a suitable weight greater than the default value (e.g., 20 for stairways and 15 for congestion). Segments with stairways and congestion may be assigned a weight that is the sum of the weight for the stair and the weight for the congestion (in this case, 35 can be assigned). On the other hand, segments that include structures for assisting the movement of pedestrians (such as motorized conveyor belts for passengers at airports) may be assigned a weight value that is smaller than the default value (e.g., 0 or -1 is assigned . In this Burger King example, applying the weights to each segment includes multiplying the length of each segment by the weights, but all appropriate operations for applying the weights may be used. The resulting overall path cost, including the application of the weight, is: 3 * W _{BK 1} + 5 * W _{BK 2} + 6 * W _BK 3 + 5 * W _{BK 4} = 3 * 1 + 5 * 1 + 6 * 1 + 19, where W _BK1 represents the weight of segment 1 in the path up to Burger King, and W _BK2 represents the weight of segment 2 in the path up to Burger King, etc.).

(18, 9, 1) -> (18, 3, 1) -> (10, 3, 1) from the current location of the user, and the length of this path is 6 + 8 = 14 (see FIG. 4). Although the distance to McDonald's is shorter than the distance to the Burger King, the segments from (18, 9, 1) to (18, 3, 1) move backwards and in the opposite direction . The movement of the pedestrian can have many attractions and features such as window shopping. The reversal of direction may not be ideal in the pedestrian environment because reversing the direction causes the user to go back to the same path he / she just moved and to see the same scenes and stores he / she has just witnessed This is because we have to. The weight value given to the inversion of direction may be assigned 10. The mobile station 100 may determine that the user has just moved from (18, 3, 1) to (18, 9, 1), for example, store the traveled path after the mobile station 100 has entered the Mall Based on the function to be performed. Therefore, a weight of 10 can be assigned to segments from (18, 9, 1) to (18, 3, 1). A weighted value of a segment from (18, 3, 1) to (10, 3, 1) may be assigned a base value of 1 (as already discussed in connection with Burger King). Therefore, the resulting overall path cost for McDonalds is 6 * 10 + 8 * 1 = 68. In another design example, instead of multiplying the segment length 6 by the weight 10, a suitable weighting value may be added to the overall path cost as a penalty for reversing the direction.

In SONIC, it is decided that the shortest distance from the user's current position is (18, 9, 1) -> (18,10,1) -> (21,10,1) -> (21,7,1) , And the path length is determined to be 1 + 3 + 3 = 7 (see FIG. 4). Although the path length to Sonic is relatively short, the segments from (18, 10, 1) to (21, 10, 1) are related to escaping the mall because Sonic is outside the mall. Thus, departing from the user's current scene is not ideal in pedestrian environments, because the user must leave the site where he or she may be accustomed and face climatic conditions that can be different from the outside. A weight of 30 for exiting the current site may be assigned. Therefore, a weight of 30 may be assigned to segments from (18, 10, 1) to (21, 10, 1). The weights for the other segments may be assigned a default value of 1 (as discussed above in connection with Burger King). The resulting overall path cost is 1 * 1 + 3 * 30 + 3 * 1 = 94.

(18, 9, 1) -> (18, 12, 1) -> (13, 12, 1) -> (13, 12, 2) -> The shortest path from the user's current position is (11, 12, 2), and the path length is determined to be 3 + 5 + 1 + 2 = 11 (see FIG. 4). Although the path length to Wendith is relatively short, the segments from (13, 12, 1) to (13, 12, 2) are related to the stairs because Wendith is located on the second floor of the mall. The stairs can provide obstacles to the pedestrian environment, because of the physical effort involved, especially when the user has a shopping bag in the case of a shopping mall. The weight assigned to the stair may be 20, and may be changed according to the number of stages of the stair, for example. The database 200 indicates that the stairs have 40 numbers. If the number of stages is large, a relatively high weight value such as 30 can be assigned. Therefore, a weight of 30 can be assigned to the segment from (13, 12, 1) to (13, 12, 2) (if the user is in a wheelchair, A large value may be assigned). The weights of the other segments may be assigned a default value of 1 (as already discussed with respect to Burger King). The resulting overall path cost is 3 * 1 + 5 * 1 + 1 * 30 + 2 * 1 = 40.

Looking at Wendy's, there is also an alternative path from the user's current location, which is also relatively short. This path is determined to be (18, 9, 1) -> (18,14,1) -> (18,14,2) -> (18,12,2) , The path length is determined to be 5 + 1 + 2 + 7 = 15 (see FIG. 4). The segments from (18, 14, 1) to (18, 14, 2) are related to the elevator, although the path length of alternative paths up to Wendith is relatively short. An elevator can provide an obstacle in the pedestrian environment, for example, due to delays associated with waiting an elevator. The weight assigned to the elevator may be 20. Therefore, a segment from (18, 14, 1) to (18, 14, 2) can be assigned a weight of 20. However, in the case where the user is riding a wheelchair, a lower value (for example, 10) may be assigned as the weight of the elevator in this case, since the elevator may be the only practical means by which the floor can be changed. In this alternative path up to Wendith, the weight of the other segments may be assigned a default value of 1 (similar to that already discussed in connection with Burger King). The resulting overall path cost is 5 * 1 + 1 * 20 + 2 * 1 + 7 * 1 = 34. This can be provided as an option to the user, since the overall path cost of this alternative path is less than the path with the shortest path length. If the user selects the map / walk guidance for Wendith, options for the stairway related path and options for the elevator related path may be provided.

As for the steakhouse, the database 200 indicates that the business hours of this place are from 5:00 pm to 10:00 pm (Monday to Saturday), and since the current time is 1:15 pm, the steak house is closed It will not open for a long time. Therefore, the location awareness recommendation engine may skip the whole path cost determination and may simply (e.g., allocate an infinite cost) to the steakhouse, for example, the highest possible total path cost. In another design example, the location awareness recommendation engine may skip the entire path cost determination of the steak house and delete it from the list of recommendations to be ranked. In another example, if the current date / time is 4:50 PM on Saturday (ten minutes before the stakehouse opens), the location awareness recommendation engine continues to determine the full path cost for the stake house and the stakehouse It may be displayed on the display 302 that it will open at 5 pm.

The path lengths from the user's current location to each entity, as returned by the relevance search are: Burger King 18, McDonald's 14, Sonic 7, Wendy's 11; And steakhouse (not applicable: restaurant closed). After considering the accessibility criteria based on the methods discussed in connection with FIG. 4, the overall path cost for each result is determined by the Burger King 18, McDonald's 68, Sonic 94, Wendy's 40 in the elevator; It is a steakhouse (infinity). Based on this overall path cost, the location awareness recommendation engine ranks the results in order of least overall path cost to largest overall path cost (i.e., in order of Burger King, Wendy's, McDonald's, Sonic, , And may provide such a ranked list to be displayed on display 302, for example. Depending on the design example, the functions discussed in connection with FIG. 4 and other functions discussed herein may be used by the MS 100, one or more servers (e.g., a server that is in direct or indirect communication with the MS 100) , Or by association with the MS 100 and one or more servers.

Figure 5 illustrates a mobile station displaying another example of location awareness alignment of recommendations. In this example, the current date / time is 6:15 PM on Saturday, and the user is at location 18, 9, 1 in the mall (see FIG. 1A). The user must buy a tennis racket and have dinner before watching the movie at 7 pm. He / she enters the recommendation request "Purchase tennis racket before movie at 7 pm movie, meal" into window 502 of the location recognition recommendation application. The recommendation engine may analyze input information using, for example, a parsing algorithm used by a conventional search engine, and may provide a recommendation for a place for a user to purchase a tennis racket and a place for eating food Time-sensitive requests with respect to time. The recommendation engine is now at 6:15 PM and the user can decide that he only has 45 minutes to buy a tennis racket and dinner. A relevance search is performed for a "tennis racket ", the result of which includes the sports chalet (SPORT CHALET) and the Sears (SEARS) as entities for sale of the tennis racket. As discussed above, the relevance search may be based on information in the database 200. The relevance search may determine that a sports chalet is more relevant than Sears because the sports chalet specializes in sporting goods and may offer more tennis racquets and better buying advice.

However, given a time constraint, the recommendation engine may determine that the shortest path length from the current user location to the sports chalet will take too much travel time, for example, And may be determined based on the average moving speed of the user that can be determined by the user. The method as described above with reference to FIG. 4 can take into account the travel time, for example, by adding a suitable value as an additional penalty at the end of the overall path cost determination for the sports chalet, if a request for a time- It can be emphasized that the sports chalet is disadvantageous. The method may rank Sears as a first recommendation for purchasing a tennis racket and display it on the display 302 using the icon 504 based on proximity to the user's current location, (504) shows the user the arrow pointing to the left indicating that he / she can turn left and reach Sears. The icon 318 next to the Sears recommendation indicates that Sears is a reasonable choice. Display 302 may display a sports chalet as a subsequent recommendation to buy a tennis racket. The icon 320 next to the sport chalet recommendation may indicate that the sports chalet is not a reasonable choice and the icon 506 may indicate to the user that he / she should hurry to go to the sports chalet. The "tennis racquet" link that exists under both Sears and Sports Chalet recommendations may indicate that more information about tennis rackets in individual stores can be accessed by selecting the link. Information about other items available in the tennis rackets and entities within the mall may include brands, models, pictures, prices, etc. (if such information is applicable) and may be stored in the database 200. Since there is no time constraint in this example, the location awareness recommendation engine can rank the sports chalet higher than the Sears (e.g., it can rank higher because the sport chalet is more relevant). In another example, a referral request may be associated with a monetary constraint, such as a recommendation for a "$ 5 meal". In this case, the recommendation engine may determine that the restaurants within the fast food sub-category of the database 200 are relevant. For restaurants that are not in the fast food sub-category, the recommendation engine may determine the amount required in these restaurants based on the menu / price information in the database 200, for example. If one of these restaurants includes choices about a reasonable number of menus, for example, $ 5 or less, then the recommendation engine may determine that the restaurant is relevant to the given recommendation request. The recommendation engine may rank related restaurants, for example, based on the method discussed with respect to FIG. 4, and provide for ranked results to be displayed at the MS 100. The recommendation engine may process a combination of other referral requests and recommendation requests (e.g., "tennis rackets less than $ 50, food available for $ 5 at 7 pm, etc." But is not limited to the side surface.

Continuing with the example shown in FIG. 5, the relevance search for food may return multiple entities of the same relevance, since various entities are all selling food in the mall. If the user has a different destination (movie theater) after eating the food, the recommendation engine may apply the full path cost method discussed in connection with FIG. 4 in a different manner. Here, instead of using the location of the user as the origin and using the location of the restaurant as the destination, a method of using the location of the restaurant as the origin and using the location of the movie theater as the destination may be applied, Because it is the most efficient use of time to guide the user to a restaurant that has the lowest overall path cost to the user's final destination, the cinema. Unless there is a time limit of 7 pm, this particular application of the method may determine that the stake house is currently open and that Wendy's and steakhouse have the lowest total path cost to the movie theater. In response to time constraints, this method may additionally determine that the steak house is a fine dining restaurant and that it may take too long to eat the food in the steak house. This method may reflect that it takes longer to eat food by applying the appropriate weight to the total path cost of the steakhouse as a penalty. For example, such a weight may be applied as part of the availability criteria, since the fact that consumers require a longer time in the steak house makes the steak house less available than Wendy's. Therefore, the recommendation engine can recommend Wendy's as the first choice for eating food before watching a movie. The icon 508 may indicate that the process of arriving at Wendith from the user's current location is related to using the stairs. The icon 318 on the display 302 may indicate that Wendy is a reasonable choice. The icon 320 on the display 302 may indicate that the steakhouse is not a reasonable choice. The icon 510 may indicate that the steakhouse is a fine dining restaurant, and thus is time consuming.

Figure 6 illustrates a mobile station displaying another example of a location based sort of recommendations. In this example, the user is at the mall location 18, 9, 1 (Fig. 1A) and needs to use the toilet. If the user looks around and does not find a toilet nearby, he / she enters "toilet" in the window 602 of the location awareness recommendation application. The recommendation engine analyzes the input information, determines that the user is looking for the nearest toilet, and implicitly interprets the request as a time-sensitive request. The relevance search results may include four toilets within the mall, all of which have the same relevance as a toilet. The recommendation engine may apply the full path cost method discussed in connection with FIG. 4 to the list of relevance search results.

The shortest path to the toilet 1 can be determined to be (18, 9, 1) -> (18, 12, 1) -> (7, 12, 1) and the path length is 3 + 11 = In this example, people are gathered near the entrance to toilet 1 (7, 12, 1) and MS 100 has the number of mobile stations (and thus the number of people) with estimated positions near (7, 12, It may be determined that there is congestion near the entrance to the toilet 1 as indicated by < RTI ID = 0.0 > The MS 100 may receive this information from the server via the wireless access point with which the MS is communicating. Therefore, the location awareness recommendation engine may determine that there is a queue for toilet 1 and may add an appropriate weight for congestion to the overall path cost determination for toilet 1. Except for such congestion in toilet 1, there is no additional accessibility criterion that can be applied to the overall path cost for toilet 1. For example, an appropriate weighting value (e.g., 50) may be added as a penalty for congestion, and the total path cost for toilet 1 may be determined to be 3 * 1 + 11 * 1 + 50 = 64. In a different example, a recommendation request for Mexican food may be entered when the user is at location 18, 9, 1. Relevance searches may determine that RUBIO'S and BAJA FRESH are equally related to Mexican food. The season may be a holiday season, and there may be many crowds gathering around ice skating links to see ice skating performances. The location awareness recommendation engine may determine that such congestion around the ice skating link interferes with the route to Rubio's and apply appropriate weighting as a penalty for such congestion. Although Baja Fresh is on the second floor and requires a layer change to arrive here, Baja Fresh may be determined as an optimal recommendation in this case.

6, the shortest route to the toilet 2 is a route including steps (18, 9, 1) -> (18, 12, 1) -> (13, 12, 1 ) -> (13, 12, 2) -> (7, 12, 2) and the path length is 3 + 5 + 1 + 6 = 15. As previously discussed with respect to FIG. 4, the weight for the staircase may be 30. Therefore, the total path cost for this path may be 3 * 1 + 5 * 1 + 1 * 30 + 6 * 1 = 44. If you select a route that includes an elevator, the route is (18, 9, 1) -> (18, 14, 1) -> (18, 14, 2) -> , 12, 2) and the path length may be determined to be 5 + 1 + 2 + 11 = 19. As discussed with respect to FIG. 4, the weight for the elevator may be 20. Therefore, the total path cost for this path may be 5 * 1 + 1 * 20 + 2 * 1 + 11 * 1 = 38. However, the path associated with the elevator may include an unpredictable waiting time for the elevator and, given the time-sensitive nature of this recommendation request, the recommendation engine may not show the corresponding path to the toilet 2 associated with the elevator , Because there are other paths (paths through the stairs) to toilet 2 that have a slightly higher overall path cost but have a more predictable travel time.

Looking at bathroom 3, database 200 indicates that bathroom 3 is located within the movie theater and is accessible only to movie viewers. Therefore, the location awareness recommendation engine may skip the entire path cost determination for toilet 3 and simply assign the highest possible path cost (e.g., a value of infinity) to toilet 3. In another example, if the location awareness recommendation engine determines that the user has a subsequent destination and that this is a movie theater (e.g., the user is planning to watch the movie), the engine can continue to determine the full path cost for toilet 3 have. In another example, the database 200 may indicate the time when the toilet is closed for cleaning. If the recommendation engine determines that the toilet is currently closed for cleaning, the engine skips the entire path cost determination for the toilet and simply assigns the highest possible path cost (e.g., a value of infinity) to the toilet May display on the display 302 an appropriate icon to indicate that this toilet has been closed. Similarly, the database 200 may indicate that one of the entities (e.g., elevator or escalator 2) is faulty, and the recommendation engine may avoid routes using the failed entity.

The shortest path up to toilet 4 can be determined to be a path that includes entering the sheath and using the escalator 1 within the sheath. In this example, Sears has its own server that can display a map for the interior of the store in local coordinates. Therefore, the database 200 has only the information that the toilet 4 is in Sears on the second floor of the mall, without the coordinates of the entrance to the toilet 4, and that this is a toilet for one person. Here, the location recognition recommendation engine may estimate the path length to the toilet 4 based on the boundaries of the sears, as shown by the database 200. Based on the database 200, the first layer of the Shearz is a rectangle having coordinates (13, 12, 1), (18, 12, 1), (18,3,1) and The second layer of Sears is divided by the rectangle with coordinates (13, 12, 2), (18, 12, 2), (18, 3, 2) The boundaries are distinguished. Based on these boundary coordinates, Sears can be estimated to be 9 unit length and 5 unit width. Thus, the worst-case scenario while moving within Sears is to go from one corner of the Sears to the diagonal corner, and the worst case path length can be 9 + 5 = 14 (move along the full length of the sears, And then move along the entire width again). The shortest path in this example is (18, 9, 1) -> (18, 8, 1) -> [(Sears first floor) -> (Escalator 1) Movement inside]. In this example, the shortest path may lead the user to enter the shear through the entrance at position 18,8, 1 located approximately at the center point in the length of the sear. Hence, the worst travel distance that this user travels to any point in the sheath can be estimated to be 1/2 * (length of sears) + (width of sears) = 1/2 * 9 + 5? 11. Moving from the user's current location on the first floor to the toilets 4 on the second floor of Sears entails weighting related to escalator 1 and escalator 1. In this example, escalator 1 may be assigned a weight of 10 as a penalty for subsequent layer change. The weight of the escalator may be less than the weight of the stairs and the elevator because the physical effort such as climbing the stairs or waiting for the elevator is not involved. In addition, an appropriate weighting factor (e.g., 10) may be added as a penalty for the complexity that must be navigated through a large entity such as Sears. Therefore, the estimated total path cost for toilet 4 can be determined to be 11 + 10 + 10 = 31. When the user enters the Sears, a map of Sears may be displayed on the display 302 to provide directions for each turn to the toilet 4. [

Based on the total path cost (64) for toilet 1, the total path cost 44 for toilet 2, the total path cost (infinity) for toilet 3, and the total path cost 31 for toilet 4, The recommendation engine can rank the toilets in order from the lowest overall path cost to the highest overall path cost, resulting in a toilet 4, a toilet 2, a toilet 1, and a toilet 3, where toilet 4 is the most optimal do. A ranked, ranked list of such search results may be provided on display 302. The icon 604 of the display 302 may indicate that the path to the toilet 4 includes an escalator. The icon 318 may indicate that toilet 4 is a reasonable choice. The icon 608 may indicate that the path to toilet 2 includes a stairway. The icon 606 may indicate that the user may have to hurry to the toilet 2 given a recommendation for a time-sensitive attribute. Icon 320 may indicate that toilet 2 is not a reasonable choice. Icon 610 may indicate that there is congestion on the path from toilet 1 to toilet 1. Icon 320 may indicate that toilet 1 is not a reasonable choice. The icon 612 may indicate that the toilet 3 is restricted in access. Icon 320 may indicate that toilet 3 is not a reasonable choice.

In some design examples, the location awareness recommendation engine may be configured through, for example, a configuration menu in the user application, or through automatic configuration by the MS 100 or server. For example, a user in a wheelchair may indicate that he / she is in a wheelchair. In response, the recommendation engine may rank entities in the same level (level / floor) as the user, higher than entities that may require layer changes. The user may also customize the weight values assigned to the various accessibility criteria. For example, the user may not hesitate to reverse the direction to reach the recommended entity, so he / she can lower the default weight associated with reversing the current direction to an appropriate value. The total path cost associated with each recommendation is configured to be displayed on the display 302 so that the user can provide information as to what recommendation is in terms of overall path cost, for example, how similar to the next recommendation on the list, This information may allow the user to make decisions that refer to more information about which recommendations to follow. The relevance of recommendation results or emphasis on location awareness may be adjusted. For example, the default setting may indicate that all recommendations should be ranked according to the overall path cost (e.g., determined based on the method discussed with respect to FIG. 4). The user can reconfigure the settings so that all the recommendations are ranked according to their relevance to the recommendation request as determined by the relevance search, for example, by closing the door, limiting access, Unless an entity is inaccessible due to an attribute, the most relevant result is listed as an optimal recommendation, and the results with the same relevance can be ranked based on the total path cost. As another example, the user may configure the maximum travel distance he / she will travel to reach the recommended entity, and any entities whose total path length from the user's current position is greater than or equal to the maximum travel distance may not be included as a recommendation . Similarly, a user may configure a maximum overall path cost that he / she will take to reach an entity. In one design example, the location awareness recommendation engine performs a location aware search upon receipt of a recommendation request to determine one or more entities that are eligible based on, for example, the user's current position, or belonging to the full path length / And rank one or more entities based on their relevance to the recommendation request.

In some design examples, the location awareness recommendation engine may automatically adjust the settings. For example, the engine may determine from the user history that the user has selected Wendy more often than Burger King, McDonald, or Sonic, or that Wendy's is the user's preferred hamburger restaurant within the mall. If later Wendith is included in the relevance search results, the recommendation engine may increase its rank to improve the likelihood of Wendith being included in a reasonable alternative, for example by reducing Wendith's overall path cost. As another example, the recommendation engine may determine from the user history that the location of the MS 100 is frequently found within the boundary coordinates of the Sears whenever the user visits the mall. The recommendation engine may determine that Sears is one of the user's preferred stores, and may make adjustments to the route to reach the recommended entities, for example, to provide the user with an opportunity to visit Sears and explore the interior This path may lead the user to approach one of the entrances of the sears.

7 is an exemplary flow chart for creating a location aware arrangement of recommendations for a pedestrian environment. At block 701, a search request may be received. At block 702, one or more search results associated with the pedestrian environment may be determined. At block 703, the location of the mobile station associated with the search request may be determined. For example, a mobile station associated with a search request may be a mobile station to which a user has entered a recommendation request, a mobile station to which an automatic recommendation request is input, and the like. For example, block 703 may be performed before block 702 or may be performed concurrently with block 702. [ At block 704, at least a portion of the one or more search results may be ranked based on at least one of a location of the MS, a location associated with the one or more search results, and / or an accessibility criterion.

8 illustrates a block diagram of a system for communicating with a mobile station that may be utilized in conjunction with a location awareness recommendation engine. MS 100 may include a transmitter / receiver (TMTR / RCVR, 802), a processing unit 804, a memory 806, sensors / camera 808, an input 810, and an output 812 have. The server 800 may include a processing unit 820, a memory 822, and a transmitter / receiver (TMTR / RCVR) The server 800 may be managed by a pedestrian scene, such as the mall in Figures 1a and 1b. The MS 100 and the server 800 may communicate over a wireless network, for example, via a wireless local area network, such as a Wi-Fi network.

MS 100 may send signaling, data, and messages to other devices, for example, via transmitter / receiver 802, and may receive, for example, signaling, data, and messages from other devices You may. The transmitter / receiver 802 may include a Wi-Fi transceiver, a cellular transceiver, a GPS receiver, a Bluetooth transceiver, a USB transceiver, and the like. The memory 806 may store information and code associated with the location awareness recommendation engine, such as a map of the site (e.g., maps such as FIGS. 1A and 1B), a database 200, a location awareness recommendation application, user history, have. Depending on the design example, the processing unit 804 may be capable of executing various functions illustrated in FIG. 7 and other functions discussed herein and / or executing them, for example, under the direction of the code stored in the memory 806 . The sensor / camera 808 may include an accelerometer, a gyroscope, an altimeter, a temperature sensor, an ambient light sensor, a digital camera, and the like (e.g., a digital camera is capable of capturing high quality images and movies). Inputs 808 include a microphone system (e.g., a noise canceling microphone system), a keypad / keyboard (e.g., keypad / keyboard 316), a display screen ), Knobs / wheels, an HDMI receiver, and the like. Output 810 may include a speaker, a display screen (e.g., display 302), a projector, a tremor / vibration generator, an HDMI transmitter, and the like. MS 100 may determine its current location and transmit its current location to server 800 as described above.

The server 800 may send signaling, data, and messages to other devices, for example, signaling, data, and messages from other devices, via the transmitter / . The transmitter / receiver 824 may include a Wi-Fi transceiver, an Ethernet connection, a Bluetooth transceiver, a USB transceiver, and the like. The memory 822 may be configured to store location information associated with the location awareness recommendation engine, such as a map of the site (e.g., maps such as FIGS. 1A and 1B), database 200, locations of Wi-Fi access points / femtocells within the site Information and code can also be stored. Depending on the design example, the processing unit 820 may be capable of executing various functions illustrated in FIG. 7 and other functions discussed herein, under the direction of code stored, for example, in the memory 822, You can also direct execution.

In the terminology used herein, a mobile station (MS) is a cellular or other wireless communication device, a personal communication system (PCS) device, a personal navigation device (PND), a personal information manager (PIM), a personal digital assistant Laptop, tablet, netbook, smartbook, or other suitable mobile devices capable of receiving wireless communication and / or navigation signals. The term "mobile station" is also intended to include devices that communicate with a personal navigation device (PND), such as by a near-field radio, infrared, wired connection, or other connection- And / or position-related processing occurs on the corresponding device or PND. It is also contemplated that the "mobile station" is intended to include any apparatus capable of communicating with a server over the Internet, Wi-Fi, or other network, such as wireless communication devices, computers, Data reception, and / or position-related processing occurs at the device, at the server, or at another device associated with the network. In addition, everything that combines the above-described devices for operability is considered a "mobile station. &Quot;

The methods discussed herein may be implemented using various means, depending on the application. For example, these methods may be implemented in the form of hardware, firmware, software, or any combination thereof. In an embodiment involving hardware, the processing unit may comprise one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs) Controllers, micro-controllers, microprocessors, electronic devices, other electronic units designed to perform the functions discussed herein, or a combination thereof.

For implementations involving firmware and / or software, the methods of the present invention may be implemented using modules (e.g., modules such as procedures, functions, etc.) that perform the functions discussed herein . Any machine readable medium that tangibly implements the instructions may be used to implement the methods discussed herein. For example, the software code may be stored in memory and executed by the processing unit. The memory may be implemented within the processing unit or external to the processing unit. The term "memory ", as used herein, refers to any type of long-term memory, short-term memory, volatile memory, non-volatile memory, or other memory and includes any particular type or number of memories, It is not limited to the type of medium.

For implementations related to firmware and / or software, functions may be stored in one or more instructions or code as computer readable media. The embodiments include a computer readable medium encoded using a data structure and a computer readable medium encoded using the computer program. The computer-readable medium may have the form of an article of manufacture. Computer readable media include physical computer storage media. The storage medium may be any available medium that can be accessed by a computer. By way of example and not of limitation, such computer-readable media can be in the form of RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage, semiconductor storage, or other storage devices, (CD), a laser disk, an optical disk, a digital disk, and the like, as well as any other medium that can be used to store the desired program code and accessible by a computer. A multifunctional disk (DVD), a floppy disk, and a Blu-ray disk, where the term generally refers to magnetically replicating data, while a disk optically replicates data using a laser . Combinations of the above-described elements should also be included within the scope of computer readable media.

In addition to being stored on a computer readable medium, the instructions and / or data may be provided as a signal on a transmission medium included within the communication device. For example, the communication device may include a transceiver having signals representative of instructions and data. These instructions and data are configured to cause one or more processing units to implement the functions highlighted in the claims. That is, the communication device includes a transmission medium having signals indicative of information for performing the disclosed functions. First, a transmission medium included in a communication device may include a first portion of information for performing the disclosed functions, and secondly a transmission medium included in the communication device may include a portion of information for performing the disclosed information 2 < / RTI >

Part of the detailed description of the invention is presented in terms of algorithms or symbolic representations of operations on binary digital signals stored in a memory in a particular device or computing device or platform for a particular application. In the context of this specification, the term specific device, special purpose device, etc., includes a general purpose computer programmed to perform certain functions in accordance with instructions from the program software. Algorithmic descriptions or symbolic representations are examples of techniques that signal processing or other techniques used by those skilled in the art to convey to others skilled in the art of their work. The term algorithm as used herein is generally considered to be a consistent sequence of operations or similar signal processing that yields the desired result. In this context, operations or processing involves physical manipulation of physical quantities. Typically, these quantities may, but need not, have the form of electrical or magnetic signals that can be stored, transferred, combined, compared, or otherwise manipulated.

Sometimes it has proven convenient to call such signals as bits, data, values, elements, symbols, letters, terms, numbers, numbers and the like, mainly for common use. It should be understood, however, that such terms and similar terms are to be associated with the appropriate physical quantities and are merely convenient designations. It will be understood by those of skill in the art that the use of terms such as "processing", "operation", "calculation", "determination" Quot; device " refers to a device ' s actions or process. Thus, in the context of this disclosure, a special purpose computer or similar special purpose electronic computing device may manipulate or translate signals, which are typically stored in memory, registers, or other information storage devices, Electronic, or magnetic physical quantities stored within the display device of the target computer or similar special purpose electronic computing device. For example, a particular computing device, special purpose device, or the like, may include a processing unit that is programmed with instructions for performing one or more specific functions.

The term "instructions, " as used herein, refers to representations that represent one or more logical operations. For example, the instructions may be "machine-readable" by being interpreted by a machine to perform one or more operations on one or more data objects. However, this is merely an example of the commands, and the claimed subject matter is not limited to this aspect. In another example, the instructions used herein may relate to encoded commands that may be executed by a processing unit having a set of commands including encoded commands. Such an instruction may be encoded in the form of a machine language understood by the processing unit. Again, these are merely examples of orders, and the claimed subject matter is not limited in this respect.

 The functions, steps and / or operations of the claims according to the embodiments of the invention described herein need not be performed in any particular order unless explicitly stated otherwise. Furthermore, although elements of the present invention may be described or claimed in the singular, plural forms are also contemplated unless explicitly limited to the singular. While there have been illustrated and discussed what are at present considered exemplary features, many other modifications may be made and equivalents may be substituted without departing from the spirit claimed herein. In addition, many modifications may be made to adapt a particular situation to the teachings of the claimed subject matter without departing from the central concept discussed herein. It is, therefore, to be understood that the claimed subject matter is not limited to the specific examples disclosed, and that these claimed subject matter may include all aspects falling within the scope of the appended claims and their equivalents.

Claims (33)

CLAIMS What is claimed is: 1. A method for ranking recommendations for a pedestrian environment,
Receiving a search request including the plurality of time sensitive requests associated with at least one time constraint indicative of a completion time associated with a plurality of time sensitive requests; And
In response to receiving the search request,
Determining search results associated with the pedestrian environment;
Determining a location of a mobile station associated with the search request; And
Ranking at least a portion of the search results based at least in part on the location of the mobile station, a location associated with at least a portion of the search results, the plurality of time sensitive requests and an accessibility criterion,
Wherein the accessibility criteria comprises a path complexity criterion and a path movement time criterion associated with the pedestrian environment and wherein the path complexity criterion is associated with the presence of one or more traversed obstacles that add complexity to the path through the pedestrian environment, Wherein the path travel time reference is associated with an amount of time to traverse the one or more obstacles associated with the path through the pedestrian environment. The method according to claim 1,
The search request includes:
User input, and / or automated search request. ≪ RTI ID = 0.0 >< / RTI > The method according to claim 1,
And providing navigation guidance to at least one of the search results. ≪ Desc / Clms Page number 21 > The method according to claim 1,
The step of determining the search results comprises:
Determining attributes of the search request;
Matching attributes of the search request with attributes of one or more entities associated with the pedestrian environment; And
And inserting each matched entity into the search results. ≪ Desc / Clms Page number 22 > The method according to claim 1,
Wherein the accessibility criteria further comprises an availability criterion. The method according to claim 1,
Wherein each accessibility criterion is assigned a weight. ≪ RTI ID = 0.0 > 8. < / RTI > The method according to claim 1,
The path travel time reference is calculated by:
A path distance between a location of the mobile station and a location associated with each of at least a portion of the search results;
speed;
Congestion on the path;
Time on stairs;
Time on the escalator; And / or
Time in the elevator
Wherein the recommendation includes at least one of: < RTI ID = 0.0 > a < / RTI > The method according to claim 1,
The path complexity criterion,
Whether the path includes an escalator;
Whether the path includes a step;
Whether the path includes an elevator;
Current direction of movement; And / or
Whether the path includes leaving the current venue
Wherein the recommendation includes at least one of: < RTI ID = 0.0 > a < / RTI > 6. The method of claim 5,
The availability criteria may include,
Whether the entity associated with the pedestrian environment is opened or closed;
Whether the entity has an access restriction;
An amount required by the entity; And / or
The time required by the entity
Wherein the recommendation includes at least one of: < RTI ID = 0.0 > a < / RTI > The method according to claim 1,
Further comprising dynamically reordering the ranked portion of the search results when the mobile station changes a position in the pedestrian environment. An apparatus for ranking recommendations for a pedestrian environment,
Comprising a processing unit,
The processing unit comprising:
Receiving a search request comprising the plurality of time sensitive requests associated with at least one time constraint indicative of a completion time associated with a plurality of time sensitive requests; And
In response to the search request,
Determine search results associated with the pedestrian environment;
Determine a location of a mobile station associated with the search request;
And rank at least a portion of the search results based at least in part on the location of the mobile station, a location associated with at least a portion of the search results, the plurality of time sensitive requests and an accessibility criterion,
Wherein the accessibility criteria comprises a path complexity criterion and a path movement time criterion associated with the pedestrian environment and wherein the path complexity criterion is associated with the presence of one or more traversed obstacles that add complexity to the path through the pedestrian environment, Wherein the path travel time reference is associated with an amount of time to traverse the one or more obstacles associated with the path through the pedestrian environment. 12. The method of claim 11,
The search request includes:
User input, and / or automated search request. ≪ RTI ID = 0.0 > 31. < / RTI > 12. The method of claim 11,
The processing unit comprising:
And provide navigation guidance to at least one of the search results. 12. The method of claim 11,
The processing unit comprising:
Determine attributes of the search request;
Match attributes of the search request with attributes of one or more entities associated with the pedestrian environment; And,
And to insert each matched entity into the search results. ≪ Desc / Clms Page number 22 > 12. The method of claim 11,
Wherein the accessibility criteria further includes an availability criterion. 12. The method of claim 11,
And wherein each accessibility criterion is assigned a weight. 12. The method of claim 11,
The path travel time reference is calculated by:
A path distance between a location of the mobile station and a location associated with each of at least a portion of the search results;
speed;
Congestion on the path;
Time on stairs;
Time on the escalator; And / or
Time in the elevator
The recommendation for the pedestrian environment. 12. The method of claim 11,
The path complexity criterion,
Whether the path includes an escalator;
Whether the path includes a step;
Whether the path includes an elevator;
Current direction of movement; And / or
Whether the path includes leaving the current scene
The recommendation for the pedestrian environment. 16. The method of claim 15,
The availability criteria may include,
Whether the entity associated with the pedestrian environment is opened or closed;
Whether the entity has an access restriction;
An amount required by the entity; And / or
The time required by the entity
The recommendation for the pedestrian environment. 12. The method of claim 11,
The processing unit comprising:
And when the mobile station changes a position in the pedestrian environment, dynamically reorders the ranked portion of the search results. An apparatus for ranking recommendations for a pedestrian environment,
Means for receiving a search request comprising the plurality of time sensitive requests associated with at least one time constraint indicating a completion time associated with a plurality of time sensitive requests;
Means for determining search results associated with the pedestrian environment in response to the search request;
Means for determining a location of a mobile station associated with the search request; And
Means for ranking at least a portion of the search results based at least in part on the location of the mobile station, a location associated with at least a portion of the search results, the plurality of time sensitive requests and an accessibility criterion,
Wherein the accessibility criteria comprises a path complexity criterion and a path movement time criterion associated with the pedestrian environment and wherein the path complexity criterion is associated with the presence of one or more traversed obstacles that add complexity to the path through the pedestrian environment, Wherein the path travel time reference is associated with an amount of time to traverse the one or more obstacles associated with the path through the pedestrian environment. 22. The method of claim 21,
The search request includes:
User input, and / or automated search request. ≪ RTI ID = 0.0 > 31. < / RTI > 22. The method of claim 21,
And means for providing navigation guidance to at least one of the search results. 22. The method of claim 21,
Wherein the means for determining the search results comprises:
Means for determining attributes of the search request;
Means for matching attributes of the search request with attributes of one or more entities associated with the pedestrian environment; And
And means for inserting each matched entity into the search results. 22. The method of claim 21,
Wherein the accessibility criteria further includes an availability criterion. 22. The method of claim 21,
And wherein each accessibility criterion is assigned a weight. 22. The method of claim 21,
The path travel time reference is calculated by:
A path distance between a location of the mobile station and a location associated with each of at least a portion of the search results;
speed;
Congestion on the path;
Time on stairs;
Time on the escalator; And / or
Time in the elevator
The recommendation for the pedestrian environment. 22. The method of claim 21,
The path complexity criterion,
Whether the path includes an escalator;
Whether the path includes a step;
Whether the path includes an elevator;
Current direction of movement; And / or
Whether the path includes leaving the current scene
The recommendation for the pedestrian environment. 26. The method of claim 25,
The availability criteria may include,
Whether the entity associated with the pedestrian environment is opened or closed;
Whether the entity has an access restriction;
An amount required by the entity; And / or
The time required by the entity
The recommendation for the pedestrian environment. 22. The method of claim 21,
And means for dynamically reordering the ranked portion of the search results when the mobile station changes a position in the pedestrian environment. Instructions for instructing the processing unit to execute a rank of recommendations for the pedestrian environment,
The instructions,
Code for receiving a search request comprising the plurality of time sensitive requests associated with at least one time constraint indicating a completion time associated with a plurality of time sensitive requests; And
In response to the search request,
Determine search results associated with the pedestrian environment;
Determine a location of a mobile station associated with the search request;
Code for ranking at least a portion of the search results based at least in part on the location of the mobile station, a location associated with at least a portion of the search results, the plurality of time sensitive requests and an accessibility criterion,
Wherein the accessibility criteria comprises a path complexity criterion and a path movement time criterion associated with the pedestrian environment and wherein the path complexity criterion is associated with the presence of one or more traversed obstacles that add complexity to the path through the pedestrian environment, Wherein the path travel time reference is associated with an amount of time to traverse the one or more obstacles associated with the path through the pedestrian environment. The method according to claim 1,
Wherein ranking at least a portion of the search results based at least in part on the location of the mobile station comprises:
Determining a direction of movement of the mobile station; And
Further comprising ranking the search results based at least in part on the direction of movement of the mobile station,
The search results in accordance with the moving direction of the mobile station and within a predetermined angle from the moving direction of the mobile station are higher than other search results that are not within a predetermined angle from the moving direction of the mobile station, A method of ranking recommendations for a pedestrian environment, wherein the recommendations are ranked. The method according to claim 1,
The search results include a monetary constraint,
The step of ranking at least a portion of the search results may include determining at least some of the search results based on at least a portion of the location of the mobile station, a location associated with at least a portion of the search results, Gt; a < / RTI > method for ranking recommendations for a pedestrian environment.

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